arXiv subjects

Brandon C. Wood

Publications and source records attributed to Brandon C. Wood.

14 recordsLinked to original sources

arXiv2024

Grand canonically optimized grain boundary phases in hexagonal close-packed titanium

Grain boundaries (GBs) profoundly influence the properties and performance of materials, emphasizing the importance of understanding the GB structure and phase behavior. As recent computational studies have demonstrated the existence of multiple GB phases associated with varying the atomic density at the interface, we introduce a validated, open-source GRand canonical Interface Predictor (GRIP) tool that automates high-throughput, grand canonical optimization of GB structures. While previous studies of GB phases have almost exclusively focused on cubic systems, we demonstrate the utility of GRIP in an application to hexagonal close-packed titanium. We perform a systematic high-throughput exploration of tilt GBs in titanium and discover previously unreported structures and phase transitions. In low-angle boundaries, we demonstrate a coupling between point defect absorption and the change in the GB dislocation network topology due to GB phase transformations, which has important implications for the accommodation of radiation-induced defects.

cond-mat.mtrl-sci↗

arXiv2023

Robust Training of Machine Learning Interatomic Potentials with Dimensionality Reduction and Stratified Sampling

Machine learning interatomic potentials (MLIPs) enable the accurate simulation of materials at larger sizes and time scales, and play increasingly important roles in the computational understanding and design of materials. However, MLIPs are only as accurate and robust as the data they are trained on. In this work, we present DImensionality-Reduced Encoded Clusters with sTratified (DIRECT) sampling as an approach to select a robust training set of structures from a large and complex configuration space. By applying DIRECT sampling on the Materials Project relaxation trajectories dataset with over one million structures and 89 elements, we develop an improved materials 3-body graph network (M3GNet) universal potential that extrapolate more reliably to unseen structures. We further show that molecular dynamics (MD) simulations with universal potentials such as M3GNet can be used in place of expensive \textit{ab initio} MD to rapidly create a large configuration space for target materials systems. Combined with DIRECT sampling, we develop a highly reliable moment tensor potential for Ti-H system without the need for iterative optimization. This work paves the way towards robust high throughput development of MLIPs across any compositional complexity.

cond-mat.mtrl-sci↗

arXiv2022

Hydrogen in Disordered Titania: Connecting Local Chemistry, Structure, and Stoichiometry through Accelerated Exploration

Hydrogen incorporation in native surface oxides of metal alloys often controls the onset of metal hydriding, with implications for materials corrosion and hydrogen storage. A key representative example is titania, which forms as a passivating layer on a variety of titanium alloys for structural and functional applications. These oxides tend to be structurally diverse, featuring polymorphic phases, grain boundaries, and amorphous regions that generate a disparate set of unique local environments for hydrogen. Here, we introduce a workflow that can efficiently and accurately navigate this complexity. First, a machine learning force field, trained on ab initio molecular dynamics simulations, was used to generate amorphous configurations. Density functional theory calculations were then performed on these structures to identify local oxygen environments, which were compared against experimental observations. Second, to classify subtle differences across the disordered configuration space, we employ a graph-based sampling procedure. Finally, local hydrogen binding energies are computed using exhaustive density functional theory calculations on representative configurations. We leverage this methodology to show that hydrogen binding energetics are described by local oxygen coordination, which in turn is affected by stoichiometry. Together these results imply that hydrogen incorporation and transport in TiO$_x$ can be tailored through compositional engineering, with implications for improving performance and durability of titanium-derived alloys in hydrogen environments.

cond-mat.mtrl-sci↗

arXiv2022

Quantifying Disorder One Atom at a Time Using an Interpretable Graph Neural Network Paradigm

Quantifying the level of atomic disorder within materials is critical to understanding how evolving local structural environments dictate performance and durability. Here, we leverage graph neural networks to define a physically interpretable metric for local disorder. This metric encodes the diversity of the local atomic configurations as a continuous spectrum between the solid and liquid phases, quantified against a distribution of thermal perturbations. We apply this novel methodology to three prototypical examples with varying levels of disorder: (1) solid-liquid interfaces, (2) polycrystalline microstructures, and (3) grain boundaries. Using elemental aluminum as a case study, we show how our paradigm can track the spatio-temporal evolution of interfaces, incorporating a mathematically defined description of the spatial boundary between order and disorder. We further show how to extract physics-preserved gradients from our continuous disorder fields, which may be used to understand and predict materials performance and failure. Overall, our framework provides an intuitive and generalizable pathway to quantify the relationship between complex local atomic structure and coarse-grained materials phenomena.

cond-mat.dis-nn↗

arXiv2022

Efficient, Interpretable Graph Neural Network Representation for Angle-dependent Properties and its Application to Optical Spectroscopy

Graph neural networks are attractive for learning properties of atomic structures thanks to their intuitive graph encoding of atoms and bonds. However, conventional encoding does not include angular information, which is critical for describing atomic arrangements in disordered systems. In this work, we extend the recently proposed ALIGNN encoding, which incorporates bond angles, to also include dihedral angles (ALIGNN-d). This simple extension leads to a memory-efficient graph representation that captures the complete geometry of atomic structures. ALIGNN-d is applied to predict the infrared optical response of dynamically disordered Cu(II) aqua complexes, leveraging the intrinsic interpretability to elucidate the relative contributions of individual structural components. Bond and dihedral angles are found to be critical contributors to the fine structure of the absorption response, with distortions representing transitions between more common geometries exhibiting the strongest absorption intensity. Future directions for further development of ALIGNN-d are discussed.

cs.LG↗

arXiv2021

Semi-Automated Creation of Density Functional Tight Binding Models Through Leveraging Chebyshev Polynomial-based Force Fields

Density Functional Tight Binding (DFTB) is an attractive method for accelerated quantum simulations of condensed matter due to its enhanced computational efficiency over standard Density Functional Theory approaches. However, DFTB models can be challenging to determine for individual systems of interest, especially for metallic and interfacial systems where different bonding arrangements can lead to significant changes in electronic states. In this regard, we have created a rapid-screening approach for determining systematically improvable DFTB interaction potentials that can yield transferable models for a variety of conditions. Our method leverages a recent reactive molecular dynamics force field where many-body interactions are represented by linear combinations of Chebyshev polynomials. This allows for the efficient creation of multi-center representations with relative ease, requiring only a small investment in initial DFT calculations. We have focused our workflow on TiH$_2$ as a model system and show that a relatively small training set based on unit-cell sized calculations yields a model accurate for both bulk and surface properties. Our approach is easy to implement and can yield accurate DFTB models over a broad range of thermodynamic conditions, where physical and chemical properties can be difficult to interrogate directly and there is historically a significant reliance on theoretical approaches for interpretation and validation of experimental results.

cond-mat.mtrl-sci↗

arXiv2020

Neural Networks Potential from the Bispectrum Component: A Case Study on Crystalline Silicon

In this article, we present a systematic study in developing machine learning force fields (MLFF) for crystalline silicon. While the main-stream approach of fitting a MLFF is to use a small and localized training sets from molecular dynamics simulation, it is unlikely to cover the global feature of the potential energy surface. To remedy this issue, we used randomly generated symmetrical crystal structures to train a more general Si-MLFF. Further, we performed substantial benchmarks among different choices of materials descriptors and regression techniques on two different sets of silicon data. Our results show that neural network potential fitting with bispectrum coefficients as the descriptor is a feasible method for obtaining accurate and transferable MLFF.

physics.comp-ph↗

arXiv2016

Self-optimizing layered hydrogen evolution catalyst with high basal-plane activity

Hydrogen is a promising energy carrier and key agent for many industrial chemical processes1. One method for generating hydrogen sustainably is via the hydrogen evolution reaction (HER), in which electrochemical reduction of protons is mediated by an appropriate catalyst-traditionally, an expensive platinum-group metal. Scalable production requires catalyst alternatives that can lower materials or processing costs while retaining the highest possible activity. Strategies have included dilute alloying of Pt2 or employing less expensive transition metal alloys, compounds or heterostructures (e.g., NiMo, metal phosphides, pyrite sulfides, encapsulated metal nanoparticles)3-5. Recently, low-cost, layered transition-metal dichalcogenides (MX2)6 based on molybdenum and tungsten have attracted substantial interest as alternative HER catalysts7-11. These materials have high intrinsic per-site HER activity; however, a significant challenge is the limited density of active sites, which are concentrated at the layer edges.8,10,11. Here we use theory to unravel electronic factors underlying catalytic activity on MX2 surfaces, and leverage the understanding to report group-5 MX2 (H-TaS2 and H-NbS2) electrocatalysts whose performance instead derives from highly active basal-plane sites. Beyond excellent catalytic activity, they are found to exhibit an unusual ability to optimize their morphology for enhanced charge transfer and accessibility of active sites as the HER proceeds. This leads to long cycle life and practical advantages for scalable processing. The resulting performance is comparable to Pt and exceeds all reported MX2 candidates.

cond-mat.mtrl-sci↗

arXiv2014

First-principles simulation of capacitive charging of graphene and implications for supercapacitor design

Supercapacitors store energy via the formation of an electric double layer, which generates a strong electric field at the electrode-electrolyte interface. Unlike conventional metallic electrodes, graphene-derived materials suffer from a low electronic density of states (i.e., quantum capacitance), which limits their ability to redistribute charge and efficiently screen this field. To explore these effects, we introduce a first-principles approach based on the effective screening medium framework, which is used to directly simulate the charge storage behavior of single- and multi-layered graphene in a way that more closely approximates operating devices. We demonstrate that the presence of the interfacial field significantly alters the capacitance in electrodes thinner than a few graphene layers, deriving in large part from intrinsic space-charge screening limitations. The capacitance is also found to be highly sensitive to the gap between the electrode and the solvent (contact layer), which offers possibilities for tuning the interfacial capacitance of the electrode by proper engineering of the electrolyte. Our results offer an alternative interpretation of discrepancies between experimental measurements and fixed-band models, and provide specific implications for improving graphene-based devices.

cond-mat.mtrl-sci↗

arXiv2014

Assessing carbon-based anodes for lithium-ion batteries: A universal description of charge-transfer binding

Many key performance characteristics of carbon-based lithium-ion battery anodes are largely determined by the strength of binding between lithium (Li) and sp2 carbon (C), which can vary significantly with subtle changes in substrate structure, chemistry, and morphology. Here, we use density functional theory calculations to investigate the interactions of Li with a wide variety of sp2 C substrates, including pristine, defective, and strained graphene; planar C clusters; nanotubes; C edges; and multilayer stacks. In almost all cases, we find a universal linear relation between the Li-C binding energy and the work required to fill previously unoccupied electronic states within the substrate. This suggests that Li capacity is predominantly determined by two key factors -- namely, intrinsic quantum capacitance limitations and the absolute placement of the Fermi level. This simple descriptor allows for straightforward prediction of the Li-C binding energy and related battery characteristics in candidate C materials based solely on the substrate electronic structure. It further suggests specific guidelines for designing more effective C-based anodes. The method should be broadly applicable to charge-transfer adsorption on planar substrates, and provides a phenomenological connection to established principles in supercapacitor and catalyst design.

cond-mat.mes-hall↗

arXiv2012

Methane and carbon dioxide adsorption on edge-functionalized graphene: A comparative DFT study

With a view towards optimizing gas storage and separation in crystalline and disordered nanoporous carbon-based materials, we use ab initio density functional theory calculations to explore the effect of chemical functionalization on gas binding to exposed edges within model carbon nanostructures. We test the geometry, energetics, and charge distribution of in-plane and out-of-plane binding of CO2 and CH4 to model zigzag graphene nanoribbons edge-functionalized with COOH, OH, NH2, H2PO3, NO2, and CH3. Although different choices for the exchange-correlation functional lead to a spread of values for the binding energy, trends across the functional groups are largely preserved for each choice, as are the final orientations of the adsorbed gas molecules. We find binding of CO2 to exceed that of CH4 by roughly a factor of two. However, the two gases follow very similar trends with changes in the attached functional group, despite different molecular symmetries. Our results indicate that the presence of NH2, H2PO3, NO2, and COOH functional groups can significantly enhance gas binding with respect to a hydrogen-passivated edge, making the edges potentially viable binding sites in materials with high concentrations of edge carbons. To first order, in-plane binding strength correlates with the larger permanent and induced dipole moments on these groups. Implications for tailoring carbon structures for increased gas uptake and improved CO2/CH4 selectivity are discussed.

cond-mat.mtrl-sci↗

arXiv2009

Dynamics and Thermodynamics of a Novel Phase of NaAlH4

We characterize a novel orthorhombic phase (gamma) of NaAlH4, discovered using first-principles molecular dynamics, and discuss its relevance to the dehydrogenation mechanism. This phase is close in energy to the known low-temperature structure and becomes the stabler phase above 320 K, thanks to a larger vibrational entropy associated with AlH4 rotational modes. The structural similarity of gamma-NaAlH4 to alpha-Na3AlH6 suggests it acts as a key intermediate during hydrogen release. Findings are consistent with recent experiments recording an unknown phase during dehydrogenation.

cond-mat.mtrl-sci↗

arXiv2007

Hydrogen Dynamics in Superprotonic CsHSO4

We present a detailed study of proton dynamics in the hydrogen-bonded superprotonic conductor CsHSO4 from first-principles molecular dynamics simulations, isolating the subtle interplay between the dynamics of the O--H chemical bonds, the O...H hydrogen bonds, and the SO4 tetrahedra in promoting proton diffusion. We find that the Grotthus mechanism of proton transport is primarily responsible for the dynamics of the chemical bonds, whereas the reorganization of the hydrogen-bond network is dominated by rapid angular hops in concert with small reorientations of the SO4 tetrahedra. Frequent proton jumping across the O--H...O complex is countered by a high rate of jump reversal, which we show is connected to the dynamics of the SO4 tetrahedra, resulting in a diminished CsHSO4/CsDSO4 isotope effect. We also find evidence of multiple timescales for SO4 reorientation events, leading to distinct diffusion mechanisms along the different crystal lattice directions. Finally, we employ graph-theoretic techniques to characterize the topology of the hydrogen-bond network and demonstrate a clear relationship between certain connectivity configurations and the likelihood for diffusive jump events.

cond-mat.mtrl-sci↗

arXiv2006

Dynamical Structure, Bonding, and Thermodynamics of the Superionic Sublattice in alpha-AgI

Using extensive first-principles molecular dynamics calculations, we characterize the superionic phase transition and the lattice and electronic structures of the archetypal Type-I superionic conductor alpha-AgI. We find that superionicity is signalled by a phase transition of the silver ions alone. In the superionic phase, the first silver shell surrounding an iodine displays a distinct dynamical structure that would escape a time-averaged characterization, and we capture this structure in a set of ordering rules. The electronic structure of the system demonstrates a unique chemical signature of the weakest-bound silver in the first shell, which in turn is most likely to diffuse. Upon melting, the silver diffusion decreases, pointing to an unusual entropic contribution to the stability of the superionic phase.

cond-mat.mtrl-sci↗